System and method for improvements to a content delivery network

ABSTRACT

Provided is a content delivery method and architecture for ways to improve the caching of content at one or more content providing devices of a Content Delivery Network (CDN). In particular, systems and methods are disclosed that vary the requirements to store resources or content within a caching device using a dynamic popularity threshold. This popularity threshold may be varied based on a measured fullness of the storage capacity of the cache device. In another example, the dynamic popularity threshold may be further varied based on a cache pressure, which is an indication of how often the cache replaces stored items with new items. The adjustment to the popularity threshold for caching particular content at the caching device may thus be based on a number of requests for content received at the device to tune the caching procedure for a particular region of the CDN.

TECHNICAL FIELD

The present disclosure relates to content delivery networks and morespecifically to improving caching within content delivery networks.

BACKGROUND

A content delivery network (CDN) is a sophisticated and large collectionof computers (e.g., content servers) and networking devices that is usedto deliver various forms of content, such as video, web pages, andimages, to devices over networks including the Internet. So, forexample, when a user operating a smart phone, laptop, tablet or othercomputing device requests a video to play on the device, a CDN may becontacted and deliver the video to the computing device where it isplayed. As more and more content is delivered over networks, refinementsand advances to CDNs, as well as the infrastructure supporting the CDN,are needed and happening constantly. Such advances involve reducingcost, increasing capacity, optimizing from where in the CDN content isdelivered and where it is stored to optimize delivery, among otherchallenges.

In order to reduce latency, CDNs may utilize a distributed network ofservers to hold data for the content providers so that when a userrequests data from the network, the request can be filled by a serverwhich is geographically closest to the user. For example, a websitecontent provider in California may deploy caches on servers located onthe East Coast, in Europe, and in Asia. Each cache can hold copies ofthe website content, such that when a user in London requests access tothe website, the user is presented the requested data from the Europeancache. By providing the content from a server geographically near therequesting device, the latency of providing the content is reduced andthe efficiency of the network is increased.

However, because the size and bandwidth of the caches of the variousservers of the CDN may be limited, systems often make determinationsregarding what content is cached and what content is stored at tiersdeeper into the network. These determinations can be further complicatedwhen the number of requests for content from a particular server exceedthe server's capacity to track information about the requests. Inaddition, determinations regarding what content should be stored can becomplicated by the use of mixed caches, where drives used to form thecache have distinct capacities and capabilities.

SUMMARY

Disclosed are systems, methods, and non-transitory computer-readablestorage media for improving caching within Content Delivery Networks(CDNs). Systems configured according to this disclosure can vary therequirements to store resources within the cache using a “dynamicpopularity threshold,” where the requirements to store resources withinthe cache vary based on how full the cache is. The dynamic popularitythreshold can be further varied based on the “cache pressure,” anindication of how often the cache needs to replace stored items with new(more popular items).

Because caching can involve a mix of different storage devices, such asSSD (Solid State Drive) and HDD (Hard Disk Drive) drives, each withtheir own storage capabilities, caching processes can select where tostore resources within the cache. Systems configured as disclosed hereincan promote or demote resources between these storage devices, such thatoverall cache performance can be enhanced.

In addition, in a proxy cache environment, caches can see an extremelylarge number of unique client requests, such that traditional methods ofmetadata storage and tracking may become ineffective. More specifically,the cache may receive a high number of requests such that the cachedevice may not have enough server memory to track information about eachrequest. Systems configured according to this disclosure can use astatistical sampling of the request stream to reduce the amount ofinformation that is tracked in server memory, such that resources whichare unpopular are statistically unlikely to pass through a statisticalsampling filter, while resources which are popular are statisticallylikely to pass through the filter and be tracked in memory.

These concepts and improvements can be used individually or incombination. For example, the promotion/demotion of elements within thecache can utilize the dynamic popularity threshold and cache pressureconcepts. Similarly, the statistical filtering of resource requests canuse the promotion/demotion elements, the cache pressure element, and/orthe dynamic popularity threshold.

One implementation of the present disclosure may take the form of amethod operating a telecommunications network. The method may includethe operations of measuring a percentage of storage capacity at a cachenode of a content delivery network (CDN) and scaling a resource cachingpopularity threshold value based on the measured percentage of storagecapacity at the cache node, the resource caching popularity thresholdvalue corresponding to a threshold of requests for a particular resourcereceived at the cache node at which the particular resource is cachedwithin a cache node storage system. The method may also includereceiving, at the cache node, a request for the particular resourceavailable from the CDN, comparing a resource popularity counterassociated with the particular resource to the scaled resource cachingpopularity threshold value, and caching the particular resource in thecache node storage system when the resource popularity counter exceedsthe scaled resource caching threshold value.

Another implementation of the present disclosure may take the form of anetworking system comprising at least one communication port forreceiving requests for content maintained by a content delivery network(CDN), a storage system for storing content of the CDN, a processingdevice, and a computer-readable medium connected to the processingdevice. The computer-readable medium is configured to store instructionsthat, when executed by the processing device, performs certainoperations. Such operations may include measuring a percentage ofstorage capacity at the storage system, scaling a content cachingpopularity threshold value for the cache node based on the measuredpercentage of storage capacity, the content caching popularity thresholdvalue corresponding to a threshold of requests for a particular contentreceived at the cache node at which the particular content is cachedwithin the storage system, comparing a content popularity counterassociated with the particular content to the scaled content cachingpopularity threshold value upon receiving a request for the particularcontent, and caching the particular content in the storage system whenthe content popularity counter exceeds the scaled content cachingthreshold value.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an example network environment for distributing content to anend user from a network, such as a content delivery network (CDN).

FIG. 2 is a flowchart of a method for scaling a threshold value based onstorage capacity utilized in a caching resource decision.

FIG. 3 is a diagram of various scaling schemes for scaling a thresholdvalue based on storage capacity utilized in a caching resource decisionof a CDN.

FIG. 4 is a flowchart of a method for applying a metadata filter basedon statistical modeling utilized in a caching resource decision.

FIG. 5 is a diagram illustrating an example of a computing system whichmay be used in implementing embodiments of the present disclosure.

DETAILED DESCRIPTION

Aspects of the present disclosure involve a content delivery method andways to improve the caching of content at one or more content providingdevices of a Content Delivery Network (CDN). In particular, systems andmethods are disclosed that vary the requirements to store resources orcontent within a caching device using a “dynamic popularity threshold”.This popularity threshold may vary based on many factors of the cachingdevice and the CDN in general. For example, the popularity threshold maybe varied based on a measured fullness of the storage capacity of thecache device. In another example, the dynamic popularity threshold maybe further varied based on a “cache pressure,” which is an indication ofhow often the cache replaces stored items with new (more popular) items.The adjustment to the popularity threshold for caching particularcontent at the caching device may thus be based on a number of requestsfor content received at the device to tune the caching procedure for aparticular region of the CDN.

In addition, because caching may involve a mix of different storagedevice types, such as SSD (Solid State Drive) and/or HDD (Hard DiskDrive) storage devices each with their own storage capabilities, cachingprocesses can select where to store resources within the cache based onthe popularity threshold value calculated for each content stored at thecache. Also, content may be promoted or demoted between the storagedevices to further enhance overall cache performance.

Also, in a proxy cache environment, some cache devices may receive anextremely large number of unique client requests such that traditionalmethods of metadata storage and tracking may become ineffective. Morespecifically, the cache may receive a high number of requests such thatthe cache device may not have enough server memory to track informationabout each request. Systems configured according to this disclosure cantherefore utilize a statistical sampling of the request stream fromrequesting devices to reduce the amount of information that is tracked(i.e., stored) in server memory, such that resources which are unpopularare statistically unlikely to pass through a statistical samplingfilter, while resources which are popular are more likely to passthrough the filter and be tracked in memory. This operates to reduce theamount of metadata storage of content that is unlikely to exceed thepopularity threshold for the cache device.

The present disclosure addresses improvements to caching for ContentDelivery Networks (CDNs). A brief introductory description of a basicCDN is provided in FIG. 1 which can be employed to practice the conceptsis disclosed herein. A more detailed description of improvements to CDNcaching will then follow.

FIG. 1 is a network environment 100 for distributing content to one ormore users. In general, the network environment 100 receives a requestfor content from a user of the network and determines a server orcontent providing component within the network to provide the content tothe user. Although illustrated in FIG. 1 as a content delivery network,it should be appreciated that aspects of the present disclosure mayapply to any type of telecommunications network that utilizes IPaddresses for connecting an end user to one or more components of thenetwork. For example, aspects of the disclosure may be utilized toconnect a user of the network to an endpoint in the network, aconferencing server, a virtual private network device, and the like.Thus, although the CDN architecture is used throughout the document asthe example network architecture through which aspects of the presentdisclosure may be applied; other network architectures andconfigurations are similarly contemplated.

In one implementation of the network environment 100, a CDN 102 iscommunicably coupled to one or more access networks 106. In general, theCDN 102 comprises one or more components configured to provide contentto a user upon a request and an underlying IP network through which therequest is received and the content is provided. The underlying IPnetwork associated with the CDN servers may be of the form of any typeIP-based communication network configured to transmit and receivecommunications through the network and may include any number and typesof telecommunications components. In this manner, CDN-based componentsmay be added to an existing IP-based communication network such that thecomponents receive a request for content, retrieve the content from astorage device, and provide the content to the requesting device throughthe supporting IP network. For simplicity, the use of the term “CDN”throughout this disclosure refers to the combination of the one or morecontent servers and the underlying IP network for processing andtransmitting communications, unless otherwise noted.

In one embodiment, a user device 104 connects to the CDN 102 through oneor more access networks 106 to request and receive content or contentfiles from the CDN. The access network 106 may be under the control ofor operated/maintained by one or more entities, such as, for example,one or more Internet Service Providers (ISPs) that provide access to theCDN 102. Thus, for example, the access network 106 may provide Internetaccess to a user device 104. In addition, the access network 106 mayinclude several connections to the IP network of the CDN 102. Forexample, access network 106 includes access point 120 and access point122. Also, the user device 104 may be connected to any number of accessnetworks 106 such that access to the CDN 102 may occur through anotheraccess network. In general, access to a CDN 102 (or underlying IPnetwork associated with the CDN) may occur through any number of ingressports to the CDN through any number of access networks. In yet anotherembodiment, the user device 104 may be a component of access network106.

The CDN 102 is capable of providing content to a user device 104, whichis generally any form of computing device, such as a personal computer,mobile device, tablet (e.g., iPad™) set-top box, or the like. Contentmay include, without limitation, videos, multimedia, images, audiofiles, text, documents, software, and other electronic resources. Theuser device 104 is configured to request, receive, process, and presentcontent. In one implementation, the user device 104 includes an Internetbrowser application with which a link (e.g., a hyperlink) to a contentitem may be selected or otherwise entered, causing a request to be sentto a directory server 110 or several such servers or devices in the CDN102.

The directory server 110 responds to the request from the end userdevice 104 or access network 106 by providing a network address (e.g.,an IP address) where the content associated with the selected link canbe obtained. In one implementation, the directory server 110 provides adomain name system (DNS) service, which resolves an alphanumeric domainname to an IP address. The directory server 110 resolves the link name(e.g., URL or other identifier) to an associated network address fromwhich the user device 104 can retrieve the content.

In one implementation, the CDN 102 includes an edge server 112, whichmay cache content from another server to make it available in a moregeographically or logically proximate location to devices, includinguser device 104, accessing the network throughout a large geographicarea . . . The edge server 112 may reduce network loads, optimizeutilization of available capacity, lower delivery costs, and/or reducecontent download time. The edge server 112 is configured to providerequested content to a requestor, which may be the user device 104possibly via an intermediate device, for example, in the access network106. In one implementation, the edge server 112 provides the requestedcontent that is locally stored in cache. In another implementation, theedge server 112 retrieves the requested content from another source,such as a media access server (MAS) (e.g., a content distribution server114 or a content origin server 116 of a content provider network 118).The content is then served to the user device 104 in response to therequests.

In addition, the CDN may broadcast to the access networks 106,108connected to the CDN information concerning the content serving nodesavailable in the CDN. In particular, the CDN may broadcast BorderGateway Protocol (BGP) information about the access path to contentserving CDN components. In general, BGP information (or BGP session, BGPfeed or BGP data) is a table of Internet Protocol (IP) prefixes whichdesignate network connectivity between autonomous systems (AS) or withinAS networks. BGP information for a network route may include path,network policies and/or rule-sets for transmission along the path, amongother information. The BGP feed may also include Interior GatewayProtocol (IGP) information for network routes within an AS or networkand/or other network information that pertains to the transmission ofcontent from the CDN.

As mentioned, the BGP information may include a path that designates thenetworks through which stored content may be accessed. In particular, aCDN may broadcast a network autonomous system (AS) identifier as part ofthe BGP announcement. The network identifier identifies the particularnetwork that the content is stored on and available to an end userdevice. Additional network autonomous system identifiers between the enduser device 104 and the stored content may be appended to or added tothe BGP path information for the content. Thus, in one embodiment, theBGP path information may provide a series of identifiers for networksthrough which the content may be accessed by the end user device 104.This information may be manipulated by the CDN or other network topotentially reduce the transmission time of the content through one ormore of the connected networks.

In one implementation, a user of the user computing device 104 enters alink name (e.g., URL or other identifier) into a browser executed on thecomputing device. The link name is associated with a network addresswithin the CDN 102 at which the content may be obtained and provided tothe computing device. For example, the user of the user device 104 mayenter a URL such as www.example.com/content into the browser of thecomputing device. Upon entering the URL, the hostname may be extractedby the browser (www.example.com in this particular case) and sends arequest (possibly via an operating system running within the computingdevice 104) to a domain name server (DNS) associated with the user'saccess network 106. Thus, the computing device 104 transmits the requestfor a content-serving device to the DNS that includes the hostname ofthe requested content.

As mentioned above, the CDN 102 may include one or more edge servers 112which may cache content from another server to make it available in amore geographically or logically proximate location to requestingdevices. However, as cache servers or devices improve, several tradeoffsmay be considered when deciding what content is cached and what contentis maintained at higher tiers of the CDN, such as mid-level or parentservers within the CDN. In particular, many cache devices or connectedcache devices (also referred to as “cache nodes”) now include both SSD(Solid State Drive) and HDD (Hard Disk Drive) storage drives, each withtheir own storage capabilities. In general, HDD storage drives havelarger storage capacity, but with a larger latency in obtaining thestored information from the drive. In contrast, SSD storage drives areoften limited in their storage capacity, but offer very low latency whenobtaining the stored data. Thus, cache nodes may determine which type ofdisk drive is utilized to store cached content for access by arequesting device. In addition, CDNs also may determine when content maybe cached and when content is maintained elsewhere in the network toimprove the operation efficiency of the overall CDN. Several mechanismsor schemes are provided below to aid a CDN in determining how and whencontent available from the CDN is cached at a cache device within theCDN and when content should be served from a higher tier of the network.

Dynamic Popularity Threshold

In one embodiment of the CDN, caching devices (such as edge device 112of CDN 102) may utilize a counter or other measurement to determine apopularity of an instance of content to determine if that content is tobe cached at an edge server or edge node. For example, edge device 112may receive any number of requests for content from any number ofcustomers 104 of the access network 106. These requests may be for thesame content or for different content available from the CDN 102. Eachrequest received at the cache device 112 for a particular content orresource may result in incrementing a counter associated with thatresource by the cache device. In one particular implementation, thecounter for a resource is incremented when a URL associated with aparticular content to access that resource is received at the cache. Forexample, user device 104 may provide URL www.example.com to the edgedevice 112 to request access to or to receive content associated withthat URL. In response, the edge device 112 may increment a counter foreach receipt of that particular URL. Other counters may also be utilizedfor other URLs and/or content available from the CDN 102. When thecounter for a specific resource hits a threshold number of requests, theproxy cache may determine that the threshold has been met and requestsand stores that resource in a local cache storage device. In oneparticular example, assume the popularity threshold for a specificcontent is set to 5. Once the proxy cache device receives a 5^(th) URLrequest for that resource, the edge device 112 will request the contentfrom a higher tier of the CDN 102 (such as from the content providernetwork 118) and store the content locally at the edge device 112. Insome instances, a time limit may be applied to the incrementing of thepopularity threshold such that the threshold may be decremented or resetto zero at the expiration of the time limit. In general, the popularitythreshold provides faster transmission of the content in response tofuture requests for the content received at the edge device, regardlessof from which user device 104 the request is received.

A potential issue with this approach is that the popularity threshold istypically static across all cache devices of the CDN 102 which may notaccount for differing traffic levels on different caches within thenetwork. That is, a cache device 112 that receives many requests, thethreshold may be exceeded and the resource cached quickly. However, onanother less busy cache device, the threshold is unlikely to be met suchthat the content is never cached or may take some time before caching.For example, a cache device for video content located within the CDN 102near New York City may receive many requests and quickly be filled dueto the number of people requesting the video. Alternatively a similarcache near Omaha, Nebr. may remain mostly empty over the same timeperiod, with the cause of the disparity largely due the relatively fewerrequests received from a smaller populace located geographically nearerthe cache device.

To remedy this circumstance, systems configured according to thisdisclosure may utilize a dynamic popularity threshold which increases ordecreases the counter threshold value associated with a particularresource based on local traffic conditions at the cache (i.e., how manyrequests are being made to the server), among other considerations. Thecounter threshold can also be based on population density and/or sizewithin a geographic threshold from the physical location of the proxyserver. For example, the initial popularity threshold for a proxy cachenear a moderately populated city may be 5 requests, whereas a proxycache near a large populated city may have a popularity threshold of 10requests. Again, the popularity threshold value may be utilized by thecache device to determine when a content or resource is stored at thecache. Thus, for the cache device near the moderately populated city,the 5^(th) request for the content received at the cache device triggersthe local storage of the content. In another example of using apopularity threshold value, the proxy cache device, while nominally setat 5 requests, may dynamically vary the value in response to measuredperformance values of the CDN, such as setting the popularity thresholdat a low value during measured low traffic conditions, to a nominalvalue during a measured average traffic condition, and to a high valueduring a measured heavy traffic condition.

One particular example for dynamically adjusting a popularity thresholdvalue of a cache device of a CDN is illustrated in the flowchart of FIG.2. In particular, FIG. 2 is a flowchart of a method 200 for utilizing acalculated percentage of used storage of a cache device or node todynamically adjust a popularity threshold value. In general, theoperations of the method 200 may be performed by one or more devices ofa CDN to aid the CDN in providing content or resources to a requestingdevice. In one particular example, an edge device (such as devicecontent server 112) of the CDN 102 may perform the operations of themethod, although any component of the CDN or in communication with theCDN may perform one or more of the operations of the method. Furtherstill, in some instances a cache of a CDN may comprise several storagedevices and be referred to as a cache node. Thus, a controllingcomponent of the CDN 102 may execute the operations of the describedmethod 200.

Beginning in operation 202, the CDN 102 calculates a storage capacitypercentage for the cache device 112 or cache node. For example, the CDN102 may determine that 50% of the total storage capacity to cachecontent at the cache device is currently utilized by the device. Inoperation 204, the CDN 102 scales a corresponding threshold popularityvalue for caching resources in the cache based on the determined storagecapacity percentage. In general, if the percentage of available storagecapacity of the cache is high, then the threshold popularity value forcaching a resource is lowered such that more resources may be cached atthe device 112 or node. Alternatively, if the percentage of availablestorage capacity of the cache is low, then the threshold popularityvalue for caching a resource may be raised so that fewer resources arecached at the node. The scaling of the popularity threshold value isdescribed in more detail below.

With the scaled threshold value determined or set for the cache node,the cache device 112 or node may begin caching resources utilizing theadjusted popularity threshold value. For example, in operation 206, thecache or edge device 112 receives a request for content from arequesting device 104, with such a request included a URL through whichthe content is available. Upon receiving the request, the CDN 102 mayincrement a popularity or request counter for that resource in operation208. In operation 210, the CDN 102 compares the popularity counter forthe requested resource to the popularity threshold value for the edgedevice 112, the popularity threshold value based at least on thepercentage of utilized cache memory space. In general, if the requestcounter value exceeds or, in some instances, equals the scaledpopularity threshold value, the resource is requested from an upper tierof the CDN 102 and cached at the cache device 112 or node. In someinstances, the CDN 102 may return to operation 202 of method 200 torecalculate the available storage capacity of the cache and being themethod 200 again for additionally received requests for content fromusers of the CDN.

The amount of adjustment or variance applied to the popularity thresholdvalue for the caching node may be calculated in many different ways.Several examples for scaling of the threshold value based on a measuredstorage capacity for a cache node and used to determine when a resourceis cached is illustrated in the diagram of FIG. 3. However, it should beappreciated that the scaling schemes illustrated in FIG. 3 are butexamples and any scaling scheme may be utilized by the CDN todynamically vary a popularity threshold for a caching node of a CDN inresponse to a measured storage capacity of the node.

The table 300 of FIG. 3 includes three scaling schemes 302-306 that maybe applied to an initial popularity threshold value of 5 for aparticular CDN caching node. It should be appreciated, however, that theinitial popularity threshold value for the caching node may be any valueand that 5 requests for caching a resource is merely used herein as anexample. Further, the scaling schemes 302-306 for the popularitythreshold value is based on four ranges 308-314 of measured cachestorage fullness (namely <50%, 50%-70%, 70%-90%, and >90%). Again, thesecache fullness ranges are selected as mere examples and any number ofranges and any percentages of utilized storage capacity may be used todefine the boundaries of those ranges. In general, the scaling of thepopularity threshold value for a caching node may be linear ornon-linear using predefined values or functions.

In example scaling scheme #1 302, the scaling factor increases by 0.5when the measured percentage fullness of the cache memory enters a newrange 308-314. For example, a measured cache fullness for the node lessthan 50% utilizes a scaling factor of 0.5 to the initial thresholdvalue. Thus, because the initial threshold value in this example is 5,the scaled or adjusted popularity threshold value for the cache when thecache fullness is less than 50% is 2.5. In other words, the cache nodemay cache a resource when a third request for the particular resource isreceived at the cache node. At some point, however, the cache fullnessmay increase to be above 50% and a new threshold value may becalculated. In particular, for a measured cache storage fullness of50%-70%, the scaling factor of 1 may be applied such that the adjustedthreshold value is 5 (the same as the initial threshold value). Similarincreases in the measured cache fullness (between 70%-90% cache fullness312) may result in the application of a scaling factor of 1.5 to theinitial threshold value, increasing the value to 7.5. Thus, as the cachestorage space is now more limited, a resource will not be cached by thecache node until the 8^(th) request for the content is received.Finally, for a measured cache fullness above 90% 314, the scaling factormay be 2 resulting in a scaled threshold value for caching a resource at10 requests.

Example scaling scheme #2 304 may take a similar approach by increasingthe scaling factor as more of the cache storage capacity is consumed.However, example scaling scheme #2 304 may not increase the scalingfactor as much as in the previous example. Here, the scaling factorincreases by 0.1 or 0.2 as the measured cache storage fullnessincreases. The scaling factor in this example may range from 1 (for ameasured cache fullness less than 50% 308) up to 1.4 (for a measuredcache fullness over 90% 314). This particular scheme may be utilizedwhen the variance of the adjusted threshold value is lessened.

Example scaling scheme #3 306 utilizes the measured storage capacitydirectly to calculate and adjust the popularity threshold value. Inparticular, in the first region 308, the popularity threshold is thecalculated percentage capacity times the initial threshold value (asshown by (% ×5) in the table 300). For the second region 310, thepopularity threshold is the calculated percentage divided by 0.5 timesthe initial threshold value. As above, the threshold value for scalingscheme #3 306 increases as more of the cache storage is consumed bycached resources.

In this manner, the overall popularity threshold (aka the effectivepopularity) can be adjusted in real-time based on actual cacheconditions. The CDN 102, or individual servers 112 themselves, cancalculate the popularity threshold based on actual conditions detectedwithin the storage of the cache device. In one example, the PopularityThreshold can be based on a function of the fullness, which can bewritten in a variety of ways:

Popularity Threshold (PT)=Scaling Factor (SF)×Default Threshold  Equation (1):

PT=function of cache Fullness (F)×function of population density  Equation (2):

Further, the popularity threshold value utilized to determine when aresource is cached can be determined using additional factors of thecache device or node.

Cache Pressure

In some instances, the popularity threshold can be further modifiedbased on a measured number of overall requests received at the cache,sometimes referred to herein as a “cache pressure”. In general, thecache pressure is determined by monitoring the activity directed at thecache device or cache node 112 in relation the storage space consumed atthe cache node. For example, the cache 112 will often delete oldresources from storage as new resources are cached or the time forretention of the old resource times out. The rate at which resources aredeleted from the cache may be measured in Mb per second and may beincluded in the cache pressure measurement discussed herein. As such,the pressure at the cache device or node may be measured throughmonitoring the writing to the storage disk of the cache node and/or theresource evictions from memory, with this measurement being used todynamically adjust the popularity threshold value.

Tying the popularity threshold value to cache pressure may provideseveral operational advantages to the caching system of the CDN 102 overprevious caching systems. For example, SSD storage drives typically havea finite amount of data that can be written to the drive over thelifetime of the drive. In one particular example, the SSD may have alifetime limit of 500 TB of data written to the drive before the drivemust be replaced. By monitoring the amount of data written to the SSDstorage device, the longevity of the lifetime of the SSD may be managed.Thus, if the popularity threshold value is increased as the pressure onthe cache to store information increases, the resources that are cachedat the cache device or node may be limited and, subsequently, limits theturnover of data in the SSD. By controlling the amount of data writtento and deleted from the SSD storage disk, the longevity of the drive maybe controlled. Further, by including cache pressure in thealgorithm/calculation of the popularity threshold, filling the cachewith resources that are no longer popular may be avoided.

One exemplary calculation of cache pressure could be:

Cache Pressure (CP)=(Data requests in the past hour)/Disk Size with amore detailed calculation of cache pressure of:   Equation (3):

CP=(Lifetime writes to the cache/Lifetime available writes)+(Datarequests in the past hour/Disk Size).   Equation (4):

Regardless of how the cache pressure on the node is calculated, thecache pressure may be included in the adjustment to the popularitythreshold value for the cache node. For example, a cache node 112 mayutilize the following equation:

PT=CP×Scaling Factor×Initial Threshold   Equation (5):

to dynamically adjust a popularity threshold value for the cache node,taking into account both the scaling factor discussed above and thecache pressure on the node. In another implementation, the cachepressure may be the only adjustment to the popularity threshold valueapplied by the cache node.

Over time, the cache device or node 112 may attain equilibrium for apopularity threshold value by monitoring the fullness factor of thestorage, the cache pressure, how long resources should be held in thecache, the effective popularity of a resource, etc. For example, a cachewhich is initially empty may rapidly cache resources being requested,with few requests not resulting in the caching of the associatedresource. However, through the embodiments of the caching system andschemes provided herein, caching of resources will become scarcer (i.e.,a resource will receive more requests or be more popular to be added tothe cache) as the cache storage begins to fill.

Popularity Promotion/Demotion

In yet another scheme to improve the caching decision in a cache node,the CDN cache 112 may utilize the different types of storage drivesassociated with the node differently. As explained above, the cachedevice or node 112 may include a mix of different storage components anddrives, such as SSDs and HDDs. Given the higher costs but the relativespeed of providing content of SSD disks, cost savings and overall systemefficiency occur when the SSDs are generally used to store the mostpopular resources, with less popular resources being stored on HDD ofthe cache node 112.

In some instances, the popularity of resources being stored in a cachenode 112 may shift over time. For example, a once popular resource whichis stored in an SSD may attain a diminished popularity at a later dateor time. Because an efficient cache node 112 stores the most popularresources on the SSDs of the node and less popular resources on theHDDs, the resource with decreased popularity may be demoted to storagein the HDD drive. Similarly, resources stored in an HDD may, when theirpopularity increases, be promoted such that it should be stored in anSSD. In other words, resources previously stored in one component shouldbe shifted to another component as the relative popularity of thoseresources shift over time.

Determinations regarding if the resource should be promoted, demoted, oreven removed can be made in conjunction with the other principlesdisclosed herein. For example, the CDN 102 or cache node 112 may utilizethe popularity threshold discussed above to make promotion/demotiondeterminations for resources on storage devices. In particular, for aresource A stored on an HDD of the cache node 112, promotion to an SSDmay occur if popularity (P) of resource A is >Threshold. In anotherexample, for a resource B stored on an SSD of the cache node 112,demotion to an HDD may occur if popularity (P) of resource B is<=Threshold. In general, however, other metrics by which a promotion ordemotion of resources from one type of storage device to another mayalso be utilized by the cache node 112 of the CDN 102. Further, ifstorage devices other than SSDs and HDDs were being used by a cache 112,similar principles may be applied of moving resources after they hadbeen cached to other types of storage components. In addition, thethreshold values utilized in the determination of promotion/demotion ofa resource may further be determined based on cache pressure as well asthe statistical filtering and caching discussed below.

Statistical Metadata Filtering

In a proxy cache environment of a CDN 102, a particular cache device ornode 112 may receive an extremely large number of unique client requestsfor content or resources (as much as billions of requests per day). Assuch, traditional approaches to metadata storage where each request isindividually stored and counted may quickly become ineffective at largenumbers of requests. That is, the cache device or node 112 may receiveso many requests for data (such as URLs) in the request stream that itmay not have enough server memory to track or store metadata for eachreceived request. This may negatively affect the performance of a cachedevice or node 112 by consuming storage space within the cacheneedlessly with metadata for resources that are not popular and isunlikely to be cached. As described above, a cache node 112 mayassociate a counter for each requested resource to determine thepopularity of a particular resource at the cache node. This counter,like a lot of resource request metadata, is stored in memory at thecache node, along with some identifier or other metadata (such as theURL of the request) to identify the resource of the request and tie thecounter to the particular requested resource. As every URL of thereceived requests is typically stored and tracked, metadata for manyun-popular resources is often stored and tracked by the cache nodealthough the counter for each resource may not be incremented byadditional requests for the resource.

To address this issue, one implementation of the CDN 102 or cache node112 may utilize statistical sampling of the incoming stream of requestsin an attempt to reduce the amount of information that is tracked incache server memory. Using this approach, resources that are unpopularare proportionally unlikely to pass through the statistical samplingfilter such that the cache node 112 can avoid wasting memory space totrack the unpopular resources. Similarly, resources that are morepopular are more likely to pass the statistical filter and be tracked inmemory.

FIG. 4 is a flowchart of a method 400 for utilizing a statistical filteron requests received at a cache node 112 of a CDN 102 to determine whichrequests are tracked by the CDN or node. In general, the operations ofthe method 400 may be performed by a cache device or cache node 112 of aCDN 102. However, any component of the CDN 102 or in communication withthe CDN may also perform one or more of the operations described.Further still, a controlling component of the CDN 102 may execute theoperations of the described method 200.

The method begins in operation 402 when the cache device or node 112receives a request from a requesting device 104 for content from thecache server, as described above. In operation 404, the cache device 112determines if the resource being requested is already cached and/orbeing tracked by the cache device. For example, a previous request forthe resource may have been received such that the cache device or nodeincludes a request counter and resource identification for thatparticular requested resource. In one implementation, the tracking ofrequests for a resource includes storing an indication of the resource(such as a URL of the resource) and a counter associated with theresource into a storage device associated with the cache node 112. Thus,the cache 112 may determine if the resource is already being tracked byaccessing the system memory and looking for the URL or other identifierfor the resource.

In operation 406, the cache device applies a statistical sampling filterto the request if the resource is not being tracked by the cache device.In general, the sampling filter operates to allow some number ofreceived requests to be tracked by the cache node 112 that is less thanall of the received requests. In particular, the cache node 112 tracksthe received requests (through the popularity counter associated withthe resource) if one request for the resource passes through thestatistical sampling filter. Various implementations of the statisticalsampling filter are described in more detail below. Once the filter isapplied to the request and the cache node determines that the resourceis to be tracked, the cache device stores and tracks requests that arereceived at the cache node 112 in operation 408. In one implementation,the storing and tracking of the resource request includes storing theURL at which the resource is available in cache system memory andassociating a counter with the URL. If the request does not pass throughthe filter, however, the cache device may not track the receivedrequest. In this manner, some of the received requests are tracked atthe cache device 112 while others are not tracked. This operates tolessen the total number of resource requests that are tracked by thecache device 112, thereby freeing up system memory from potentialtracking of unpopular resources in the cache system memory.

In one particular implementation, the statistical filter for filteringout tracking of resources in the cache device can operate based on acombination of randomness and/or a popularity curve. For example, if 10%of all data requests result in caching, and 90% of all data requests donot result in caching, systems configured with this filter areimplemented to have a 10% chance of storing any individual request'smetadata. However, for a system that receives a billion or morerequests, tracking 10% of such requests may provide the proper balanceto request filtering as desired by a system administrator. In general,the target percentage of received requests to allow through thestatistical filter may be selected as any percentage desired by anadministrator or device of the CDN 102.

To execute one particular statistical filter of the cache node 112, arequest may be received at the node and a random number between 1 and100 may be generated. If the random number is above a threshold value(such as value 10), the metadata associated with the request (and anyassociated counting of the individual request to determine popularity)is ignored. However, if the random number is at or below threshold value10, the metadata associated with the request (and any count associatedwith the resource) is stored. This is but a simple example of astatistical filter that may be applied at a cache node 112 of a CDN 102.Those of ordinary skill in the art will recognize other filteringmechanisms that may be utilized to reduce the number of resourcerequests that are tracked and stored by the cache node 112.

Over time, the statistical filter operates to produce statisticallysimilar results as counting each individual resource (i.e., knowingwhich resources are popular), but with the benefit of reducing thenumber of server resources required to track the data. For example, themost popular resources will receive hundreds or thousands of requestsfrom various requesting devices connected to the CDN 102. Thus, it isstatistically likely that at least one of those requests pass throughthe filter such that tracking of the resource requests may be conductedby the cache node 112. Alternatively, the cache node 112 may receiveonly a few requests for resources that are not popular such that it isstatistically likely that these requests will be filtered out and notstored or tracked in the cache device storage. In this manner, requestsfor the most popular resources may be tracked without requests for lesspopular resources consuming large amounts of storage space in the cachenode 112. Further, this filtering process may be used with the conceptsof dynamic popularity thresholds, cache pressure monitoring, andpromotion/demotion as outlined above.

Although discussed above with reference to a random number generator,any type of statistical sampling filter may be used. Further, the filtermay be tuned to be more or less inclusive as desired by an operator ofthe cache device or the cache device itself. For example, the cachedevice may track only one in every 100 resource requests at some times,and one in every 10 resources requests at other times. Further, thetuning of the filter may be based on other metrics or calculationsperformed by the cache device. For example, if the cache pressure ismeasured to be high, the filter may be tuned to be less inclusive (ortrack fewer resource requests) to ease the tracking of the resources bythe cache device. Alternatively, the filter may be tuned to be moreinclusive to track resources, perhaps when a measurement of the systemmemory usage is determined to be low. In general, any performancemeasurement may be utilized by the cache device in tuning the filter.

Through the schemes discussed above, the cache device or node 112 mayenhance the caching of resources at the edge server or other cachedevice. In this manner, more efficient caching of resources may beperformed to improve the overall performance of the CDN 102.

FIG. 4 is a block diagram illustrating an example of a computing deviceor computer system 400 which may be used in implementing the embodimentsof the network disclosed above. In particular, the computing device ofFIG. 4 is one embodiment of the server or other networking componentthat performs one of more of the operations described above. Thecomputer system (system) includes one or more processors 402-406.Processors 402-406 may include one or more internal levels of cache (notshown) and a bus controller or bus interface unit to direct interactionwith the processor bus 412. Processor bus 412, also known as the hostbus or the front side bus, may be used to couple the processors 402-406with the system interface 414. System interface 414 may be connected tothe processor bus 412 to interface other components of the system 400with the processor bus 412. For example, system interface 414 mayinclude a memory controller 418 for interfacing a main memory 416 withthe processor bus 412. The main memory 416 typically includes one ormore memory cards and a control circuit (not shown). System interface414 may also include an input/output (I/O) interface 420 to interfaceone or more I/O bridges or I/O devices with the processor bus 412. Oneor more I/O controllers and/or I/O devices may be connected with the I/Obus 426, such as I/O controller 428 and I/O device 430, as illustrated.

I/O device 430 may also include an input device (not shown), such as analphanumeric input device, including alphanumeric and other keys forcommunicating information and/or command selections to the processors402-406. Another type of user input device includes cursor control, suchas a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to the processors 402-406and for controlling cursor movement on the display device.

System 400 may include a dynamic storage device, referred to as mainmemory 416, or a random access memory (RAM) or other computer-readabledevices coupled to the processor bus 412 for storing information andinstructions to be executed by the processors 402-406. Main memory 416also may be used for storing temporary variables or other intermediateinformation during execution of instructions by the processors 402-406.System 400 may include a read only memory (ROM) and/or other staticstorage device coupled to the processor bus 412 for storing staticinformation and instructions for the processors 402-406. The system setforth in FIG. 4 is but one possible example of a computer system thatmay employ or be configured in accordance with aspects of the presentdisclosure.

According to one embodiment, the above techniques may be performed bycomputer system 400 in response to processor 404 executing one or moresequences of one or more instructions contained in main memory 416.These instructions may be read into main memory 416 from anothermachine-readable medium, such as a storage device. Execution of thesequences of instructions contained in main memory 416 may causeprocessors 402-406 to perform the process steps described herein. Inalternative embodiments, circuitry may be used in place of or incombination with the software instructions. Thus, embodiments of thepresent disclosure may include both hardware and software components.

A machine readable medium includes any mechanism for storing ortransmitting information in a form (e.g., software, processingapplication) readable by a machine (e.g., a computer). Such media maytake the form of, but is not limited to, non-volatile media and volatilemedia. Non-volatile media includes optical or magnetic disks. Volatilemedia includes dynamic memory, such as main memory 416. Common forms ofmachine-readable medium may include, but is not limited to, magneticstorage medium (e.g., floppy diskette); optical storage medium (e.g.,CD-ROM); magneto-optical storage medium; read only memory (ROM); randomaccess memory (RAM); erasable programmable memory (e.g., EPROM andEEPROM); flash memory; or other types of medium suitable for storingelectronic instructions.

Embodiments of the present disclosure include various steps, which aredescribed in this specification. The steps may be performed by hardwarecomponents or may be embodied in machine-executable instructions, whichmay be used to cause a general-purpose or special-purpose processorprogrammed with the instructions to perform the steps. Alternatively,the steps may be performed by a combination of hardware, software and/orfirmware.

Various modifications and additions can be made to the exemplaryembodiments discussed without departing from the scope of the presentinvention. For example, while the embodiments described above refer toparticular features, the scope of this invention also includesembodiments having different combinations of features and embodimentsthat do not include all of the described features. Accordingly, thescope of the present invention is intended to embrace all suchalternatives, modifications, and variations together with allequivalents thereof.

Embodiments within the scope of the present disclosure may also includetangible and/or non-transitory computer-readable storage media forcarrying or having computer-executable instructions or data structuresstored thereon. Such tangible computer-readable storage media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer, including the functional design of any special purposeprocessor as described above. By way of example, and not limitation,such tangible computer-readable media can include RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to carryor store desired program code means in the form of computer-executableinstructions, data structures, or processor chip design. Wheninformation is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or combinationthereof) to a computer, the computer properly views the connection as acomputer-readable medium. Thus, any such connection is properly termed acomputer-readable medium. Combinations of the above should also beincluded within the scope of the computer-readable media.

Computer-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,components, data structures, objects, and the functions inherent in thedesign of special-purpose processors, etc. that perform particular tasksor implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

Other embodiments of the disclosure may be practiced in networkcomputing environments with many types of computer systemconfigurations, including personal computers, hand-held devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, network PCs, minicomputers, mainframe computers, and thelike. Embodiments may also be practiced in distributed computingenvironments where tasks are performed by local and remote processingdevices that are linked (either by hardwired links, wireless links, orby a combination thereof) through a communications network. In adistributed computing environment, program modules may be located inboth local and remote memory storage devices.

The various embodiments described above are provided by way ofillustration only and should not be construed to limit the scope of thedisclosure. For example, the principles herein can be extended to alltypes of backup networks, and are not exclusive to content deliverynetworks. Various modifications and changes may be made to theprinciples described herein without following the example embodimentsand applications illustrated and described herein, and without departingfrom the spirit and scope of the disclosure.

What is claimed is:
 1. A method for operating a telecommunicationsnetwork comprising: determining a percentage of storage capacity at acache node of a content delivery network (CDN); determining lifetimeavailable writes for the cache node, wherein lifetime available writescomprises a total amount of data that can be written to the cache nodebefore the cache node must be replaced; scaling a resource cachingpopularity threshold value based on both the determined percentage ofstorage capacity at the cache node and cache pressure for the cachenode, wherein scaling the resource caching popularity threshold valuecomprises determining a threshold popularity above which a particularresource will be cached at the cache node, and wherein cache pressurefor the cache node is a function of a ratio between total writes to thecache node and the lifetime available writes to the cache node; andcaching the particular resource in the cache node storage system when aresource popularity counter exceeds the scaled resource cachingthreshold value.
 2. The method of claim 1 wherein the percentage ofstorage capacity at the cache node is within a first range of storagecapacity percentages and scaling the resource caching popularitythreshold value based on the determined percentage of storage capacityat the cache node comprises: applying a first scaling factor to theresource caching popularity threshold value, the first scaling factorcorresponding to the first range of storage capacity percentages.
 3. Themethod of claim 2 wherein the percentage of storage capacity at thecache node is within a second range of storage capacity percentages andscaling the resource caching popularity threshold value based on thedetermined percentage of storage capacity at the cache node comprises:applying a second scaling factor to the resource caching popularitythreshold value, the second scaling factor corresponding to the secondrange of storage capacity percentages and different than the firstscaling factor.
 4. The method of claim 1 further comprising: calculatinga deletion rate at which resources are deleted from the cache nodestorage system over a period of time; and adjusting the resource cachingpopularity threshold value based at least on the calculated deletionrate of the resources from the cache node storage system.
 5. The methodof claim 1 wherein the cache node storage system comprises a first typeof storage drive and a second type of storage drive, the first type ofstorage drive different than the second type of storage drive, themethod further comprising: storing a first resource in the first type ofstorage drive; and storing a second resource in the second type ofstorage drive.
 6. The method of claim 5 further comprising: promotingthe first resource from the first type of storage drive to the secondtype of storage drive when a popularity index for the first resource isgreater than the resource caching popularity threshold value.
 7. Themethod of claim 5 further comprising: demoting the second resource fromthe second type of storage drive to the first type of storage drive whena popularity index for the second resource is less than or equal to theresource caching popularity threshold value.
 8. The method of claim 1further comprising: applying a request for the particular resourceavailable from the CDN to a statistical sampling filter; and generatingthe resource popularity counter for the particular resource if therequest succeeds the statistical sampling filter.
 9. The method of claim8 wherein the statistical sampling filter comprises generating a randomnumber value and passing the request for the particular resourceavailable from the CDN through the statistical sampling filter if therandom number value is less than a filter threshold value.
 10. Anetworking system comprising: at least one communication port forreceiving requests for content maintained by a content delivery network(CDN); a storage system for storing content of the CDN; a processingdevice; and a nontransitory computer-readable medium operably connectedto the processing device, the nontransitory computer-readable mediumconfigured to store instructions that, when executed by the processingdevice, cause the processing device to perform the operations of:determining a percentage of storage capacity at the storage system;determining lifetime available writes for the storage system, whereinlifetime available writes comprises a total amount of data that can bewritten to the storage system before the storage system must bereplaced; scaling a content caching popularity threshold value for thestorage system based on both the determined percentage of storagecapacity and cache pressure for the cache node storage system, whereinscaling the content caching popularity threshold value comprisesdetermining a threshold popularity above which particular content willbe cached at the storage system, and wherein cache pressure for thecache node storage system is a function of a ratio between total writesto the cache node storage system and the lifetime available writes tothe cache node storage system; and caching the particular content in thestorage system when the content popularity counter exceeds the scaledcontent caching threshold value.
 11. The networking system of claim 10further comprising: a first storage device comprising a solid state typememory drive storing a first resource; and a second storage devicecomprising a hard disk type memory drive storing a second resourcedifferent than the first resource.
 12. The networking system of claim 11wherein the processing device further performs the operation of:promoting the second resource from the hard disk type memory drive tothe solid state type memory drive when a popularity index for the secondresource is greater than the resource caching popularity thresholdvalue.
 13. The networking system of claim 11 wherein the processingdevice further performs the operation of: demoting the first resourcefrom the solid state type memory drive to the hard disk type memorydrive when a popularity index for the first resource is less than orequal to the resource caching popularity threshold value.
 14. Thenetworking system of claim 10 wherein the percentage of storage capacityis within a first range of storage capacity percentages and scaling thecontent caching popularity threshold value comprises: applying a firstscaling factor to the content caching popularity threshold value, thefirst scaling factor corresponding to the first range of storagecapacity percentages.
 15. The networking system of claim 14 whereinscaling the content caching popularity threshold value with the firstscaling factor results in a scaled content caching popularity thresholdvalue that is less than an initial content caching popularity thresholdvalue.
 16. The networking of claim 14 wherein the percentage of storageis within a second range of storage capacity percentages and scaling thecontent caching popularity threshold value comprises: applying a secondscaling factor to the content caching popularity threshold value, thesecond scaling factor corresponding to the second range of storagecapacity percentages and different than the first scaling factor. 17.The networking system of claim 16 wherein scaling the content cachingpopularity threshold value with the second scaling factor causes thescaled content caching popularity threshold value to increase.
 18. Thenetworking system of claim 10 wherein the processing device furtherperforms the operations of: calculating a deletion rate at which contentis deleted from the storage system over a period of time; and adjustingthe content caching popularity threshold value based at least on thecalculated deletion rate of the content from the storage system.
 19. Thenetworking system of claim 10 wherein the processing device furtherperforms the operations of: applying a statistical sampling filter to arequest for the particular content; and generating the contentpopularity counter for the particular content if the request passes thestatistical sampling filter.
 20. The networking system of claim 19wherein the statistical sampling filter comprises generating a randomnumber value and passing the request for the particular content throughthe statistical sampling filter if the random number value is less thana filter threshold value.